US20060093233A1 - Ringing reduction apparatus and computer-readable recording medium having ringing reduction program recorded therein - Google Patents

Ringing reduction apparatus and computer-readable recording medium having ringing reduction program recorded therein Download PDF

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US20060093233A1
US20060093233A1 US11/258,354 US25835405A US2006093233A1 US 20060093233 A1 US20060093233 A1 US 20060093233A1 US 25835405 A US25835405 A US 25835405A US 2006093233 A1 US2006093233 A1 US 2006093233A1
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image
restoration
pixel
weighted average
edge intensity
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Hiroshi Kano
Ryuuichirou Tominaga
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Sanyo Electric Co Ltd
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Sanyo Electric Co Ltd
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Assigned to SANYO ELECTRIC CO., LTD. reassignment SANYO ELECTRIC CO., LTD. ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: TOMINAGA, RYUUICHIROU, KANO, HIROSHI
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
    • H04N23/60—Control of cameras or camera modules
    • H04N23/68—Control of cameras or camera modules for stable pick-up of the scene, e.g. compensating for camera body vibrations
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
    • H04N23/60—Control of cameras or camera modules
    • H04N23/68—Control of cameras or camera modules for stable pick-up of the scene, e.g. compensating for camera body vibrations
    • H04N23/682—Vibration or motion blur correction

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  • the present invention relates to a ringing reduction apparatus and a computer-readable recording medium having a ringing reduction program recorded therein.
  • a still image camera shake correction technology reduces blurring of images due to hand movement while taking still images.
  • a hand movement (camera shake) is detected and an image is stabilized based on the detection result, thereby realizing the still image camera shake correction technology.
  • a method of detecting the camera shake includes a method in which a camera shake sensor (angular velocity sensor) is used and an electronic method of analyzing the image to detect the camera shake.
  • a method of stabilizing the image includes an optical method of stabilizing a lens and an image pickup device and an electronic method of reducing blurring caused by the camera, shake by image processing.
  • the full-electronic camera shake correction technology i.e., analyzing and processing only one image with camera shake blurring and thereby generating an image with reduced camera shake blurring has not yet been developed to a practical level. Particularly it is difficult that a camera shake signal having accuracy obtained by a camera shake sensor is determined by analyzing one image with camera shake blurring.
  • the camera shake is detected by the camera shake sensor and the camera shake blurring is reduced by the image processing with the camera shake data.
  • the burring reduction performed by the image processing is called image restoration.
  • a technique performed by the camera shake sensor and the image restoration shall be called electronic camera shake correction.
  • an image restoration filter such as a Wiener filter and a general inverse filter.
  • an undulated degradation called ringing which is of an adverse effect is generated on the periphery of an edge portion of the image.
  • the ringing is a phenomenon similar to overshoot and undershoot on the periphery of the edge portion. The overshoot and undershoot are seen in simple edge enhancement processing, unsharp masking, and the like.
  • An object of the invention is to provide a ringing reduction apparatus that can reduce the ringing generated in the image restore with the image restoration filter and a computer-readable recording medium having a ringing reduction program recorded therein.
  • a first aspect of the invention is a ringing reduction apparatus including image restoration means for restoring an input image with image degradation to the image with less degradation using an image restoration filter; and weighted average means for performing weighted average of the input image and the restoration image obtained by the image restoration means, wherein the weighted average means performs the weighted average of the input image and the restoration image such that a degree of the input image is strengthened in a portion where ringing is conspicuous in the restoration image, and the weighted average means performs the weighted average of the input image and the restoration image such that a degree of the restoration image is strengthened in a portion where the ringing is inconspicuous in the restoration image.
  • a second aspect of the invention is a ringing reduction apparatus including image restoration means for restoring an input image with image degradation to the image with less degradation using an image restoration filter; edge intensity computing means for computing edge intensity in each pixel of the input image; and weighted average means for performing weighted average of the input image and the restoration image obtained by the image restoration means in each pixel based on the edge intensity in each pixel computed by the edge intensity computing means, wherein the weighted average means performs the weighted average of the input image and the restoration image such that a degree of the input image is strengthened for the pixel having the small edge intensity, and the weighted average means performs the weighted average of the input image and the restoration image such that a degree of the restoration image is strengthened for the pixel having the large edge intensity.
  • a third aspect of the invention is a ringing reduction apparatus including edge intensity computing means for computing edge intensity in each pixel of an input image with image degradation; selection means for selecting one image restoration filter in each pixel from plural image restoration filters having different degrees of image restoration intensity based on the edge intensity in each pixel computed by the edge intensity computing means; and image restoration means for restoring a pixel value of each pixel of the input image to the pixel value with less degradation using the image restoration filter selected for the pixel, wherein the selection means selects the image restoration filter having weak restoration intensity for the pixel having the small edge intensity, and the selection means selects the image restoration filter having strong restoration intensity for the pixel having the large edge intensity.
  • a fourth aspect of the invention is a computer-readable recording medium having a ringing reduction program recorded therein, wherein the ringing reduction program for causing a computer to function as image restoration means for restoring an input image with image degradation to the image with less degradation using an image restoration filter; and weighted average means for performing weighted average of the input image and the restoration image obtained by the image restoration means, is recorded in the computer-readable recording medium, the weighted average means performs the weighted average of the input image and the restoration image such that a degree of the input image is strengthened in a portion where ringing is conspicuous in the restoration image, and the weighted average means performs the weighted average of the input image and the restoration image such that a degree of the restoration image is strengthened in a portion where the ringing is inconspicuous in the restoration image.
  • a fifth aspect of the invention is a computer-readable recording medium having a ringing reduction program recorded therein, wherein the ringing reduction program for causing a computer to function as image restoration means for restoring an input image with image degradation to the image with less degradation using an image restoration filter; edge intensity computing means for computing edge intensity in each pixel of the input image; and weighted average means for performing weighted average of the input image and the restoration image obtained by the image restoration means in each pixel based on the edge intensity in each pixel computed by the edge intensity computing means, is recorded in the computer-readable recording medium, the weighted average means performs the weighted average of the input image and the restoration image such that a degree of the input image is strengthened for the pixel having the small edge intensity, and the weighted average means performs the weighted average of the input image and the restoration image such that a degree of the restoration image is strengthened for the pixel having the large edge intensity.
  • a sixth aspect of the invention is a computer-readable recording medium having a ringing reduction program recorded therein, wherein the ringing reduction program for causing a computer to function as edge intensity computing means for computing edge intensity in each pixel of an input image with image degradation; selection means for selecting one image restoration filter in each pixel from plural image restoration filters having different degrees of image restoration intensity based on the edge intensity in each pixel computed by the edge intensity computing means; and image restoration means for restoring a pixel value of each pixel of the input image to the pixel value with less degradation using the image restoration filter selected for the pixel, is recorded in the computer-readable recording medium, the selection means selects the image restoration filter having weak restoration intensity for the pixel having the small edge intensity, and the selection means selects the image restoration filter having strong restoration intensity for the pixel having the large edge intensity.
  • FIG. 1 is a block diagram showing a configuration of a camera shake correction processing circuit provided in a digital camera
  • FIG. 2 is a block diagram showing an amplifier which amplifies output of an angular velocity sensor 1 a and an A/D converter which converts amplifier output into a digital value;
  • FIG. 3 is a schematic view showing a relationship between a rotating amount ⁇ (deg) of camera and a moving amount d (mm) on a screen;
  • FIG. 4 is a schematic view showing a 35 mm film-conversion image-size and an image size of the digital camera
  • FIG. 5 is a schematic view showing a spatial filter (PSF) which expresses camera shake
  • FIG. 6 is a schematic view for explaining Bresenham line-drawing algorithm
  • FIG. 7 is a schematic view showing PSF obtained by a motion vector
  • FIG. 8 is a schematic view showing a 3 ⁇ 3 area centered on a target pixel v 22 ;
  • FIGS. 9A and 9B are a schematic view showing a Prewitt edge extraction operator.
  • FIG. 10 is a graph showing a relationship edge intensity v_edge and a weighted average coefficient k.
  • FIG. 1 shows a configuration of a camera shake correction processing circuit provided in the digital camera.
  • Reference numerals 1 a and 1 b designate angular velocity sensors which detect angular velocity.
  • the angular velocity sensor 1 a detects the angular velocity in a pan direction of the camera
  • the angular velocity sensor 1 b detects the angular velocity in a tilt direction of the camera.
  • Numeral 2 designates an image restoration filter computing unit which computes an image restoration filter coefficient based on the two-axis angular velocity detected by the angular velocity sensors 1 a and 1 b.
  • Numeral 3 designates an image restoration processing unit which performs image restoration processing to the pickup image (camera shake image) based on the coefficient computed by the image restoration filter computing unit 2 .
  • Numeral 4 designates a ringing reduction processing unit which reduces the ringing from the restoration image obtained by the image restoration processing unit 3 .
  • Numeral 5 designates an unsharp masking processing unit which performs unsharp masking processing to the image obtained by the ringing reduction processing unit 4 .
  • the following describes the image restoration filter computing unit 2 , the image restoration processing unit 3 , and the ringing reduction processing unit 4 .
  • the image restoration filter computing unit 2 includes a camera shake signal/motion vector conversion processing unit 21 , a motion vector/camera shake function conversion processing unit 22 , and a camera shake function/general inverse filter conversion processing unit 23 .
  • the camera shake signal/motion vector conversion processing unit 21 converts angular velocity data (camera shake signal) detected by the angular velocity sensors 1 a and 1 b into a motion vector.
  • the motion vector/camera shake function conversion processing unit 22 converts the motion vector obtained by the camera shake signal/motion vector conversion processing unit 21 into a camera shake function (PSF: Point Spread Function) expressing image blurring.
  • PSD Point Spread Function
  • the camera shake function/general inverse filter conversion processing unit 23 converts the camera shake function obtained by the motion vector/camera shake function conversion processing unit 22 into a general inverse filter (image restoration filter).
  • the original data of the camera shake is the pieces of output data of the angular velocity sensors 1 a and 1 b between shooting start and shooting end.
  • the angular velocities in the pan and tilt directions are measured at predetermined sampling intervals dt (s) using the angular velocity sensors 1 a and 1 b, and the data is obtained until the shooting is ended.
  • the sampling interval dt (S) is 1 ms.
  • an angular velocity ⁇ ′ (deg/s) in the pan direction of the camera is converted into a voltage V g (mV) by the angular velocity sensor 1 a, and then the voltage V g is amplified by an amplifier 101 .
  • a voltage V a (mV) outputted from the amplifier 101 is converted into a digital value D L (step) by an A/D converter 102 .
  • the computation is performed with sensor sensitivity S (mV/deg/s), an amplifier amplification factor K (time) and an A/D conversion coefficient L (mV/step).
  • the amplifier and the A/D converter are provided in each of the angular velocity sensors 1 a and 1 b.
  • the amplifiers and the A/D converters are provided in the camera shake signal/motion vector conversion processing unit 21 .
  • V a KV g
  • the angular velocity can be determined from the sensor data by using the above expressions (1) to (3).
  • ⁇ ′ ( L/KS ) D L (4)
  • a rotating amount generated in the camera between one sample value and the subsequent sample value in the angular velocity data is set ⁇ (deg). Between one sample value and the subsequent sample value, it is assumed that the camera is rotated while the angular velocity is kept constant.
  • a focal distance (35 mm film conversion) is set at r (mm)
  • a moving amount d (mm) on the screen is determined from the rotating amount ⁇ (deg) of the camera by the following expression (6).
  • d r tan ⁇ (6)
  • the determined moving amount d (mm) is magnitude of the camera shake in the 35 mm film conversion, and unit is (mm).
  • the image size is considered in unit (pixel) of the image size of the digital camera.
  • the 35 mm film-conversion image differs from the image in unit (pixel) taken with the digital camera in an aspect ratio, so that the following computation is performed.
  • 36 (mm) ⁇ 24 (mm) is defined as a horizontal to vertical ratio of the image size.
  • the size of the image taken with the digital camera is set at X (pixel) ⁇ Y (pixel), the blurring in the horizontal direction (pan direction) is set at x (pixel), and the blurring in the vertical direction (tilt direction) is set at y (pixel).
  • the conversion equations become the following expressions (7) and (8).
  • suffixes x and y are used in d and ⁇ .
  • the suffix x indicates the value in the horizontal direction
  • the suffix y indicates the value in the vertical direction.
  • the burring amount of image can be determined from the angular velocity data of each axis of the camera, obtained in the form of the digital value, by using the conversion equations (9) and (10).
  • the motion vectors during the shooting can be obtained to the number of pieces of angular velocity data (the number of sample points) obtained from the sensor.
  • a camera shake locus on the image is obtained.
  • the velocity of the camera shake at that point is learned by checking the magnitude of each vector.
  • the camera shake can be expressed by using a spatial filter.
  • spatial filter processing is performed by weighting the element of the operator in accordance with the camera shake locus (the locus drawn by one point on the image when the camera is shaken, the blurring amount of image) shown on the left side of FIG. 5 , because only a gray value of the pixel near the camera shake locus is considered in the filtering process, the camera shake image can be produced.
  • PSF Point Spread Function
  • the weight of each element of PSF is the value proportional to a time when the camera shake locus passes through the element, and the weight of each element of PSF is the value which is normalized such that a summation of the weights of the elements becomes one. That is, the weight of each element of PSF is set at the weight which is proportional to an inverse number of the magnitude of the motion vector. This is because the position which is moved more slowly has the large influence on the image in consideration of the influence of the camera shake on the image.
  • the center of FIG. 5 shows PSF in the case where it is assumed that the camera shake is moved at constant speed
  • the right side of FIG. 5 shows PSF in the case where the magnitude of the actual camera shake motion is considered.
  • the element in which the weight of PSF is low (the magnitude of the motion vector is large) is indicated by black
  • the element in which the weight of PSF is high (the magnitude of the motion vector is small) is indicated by white.
  • the motion vector (blurring amount of image) obtained in the above (2-1) has a locus of the camera shake and a camera shake velocity in the form of the data.
  • a weighted element in PSF is determined from the camera shake locus. Then, the weight applied to the element of PSF is determined from the camera shake velocity.
  • the locus has accuracy not more than a fractional part
  • the element weighted in PSF is determined by rounding the locus to the whole number. Therefore, in the embodiment, the element weighted in PSF is determined with Bresenham line-drawing algorithm.
  • the Bresenham line-drawing algorithm is one which selects the optimum dot position when a straight line passing through two arbitrary points is drawn on the digital screen.
  • the Bresenham line-drawing algorithm will be described with reference to FIG. 6 .
  • a straight line with an arrow indicates the motion vector.
  • the straight line through which the motion vector passes can be expressed with the dot positions by repeating the above processes up to the end point of the motion vector.
  • the weight applied to the element of PSF is determined by utilizing difference in magnitude of the vector (velocity component) in each motion vector.
  • the weight is the inverse number of the magnitude of the motion vector, and is substituted for the element corresponding to each motion vector.
  • the weight of each element is normalized such that the summation of the weights of the elements becomes one.
  • FIG. 7 shows PSF obtained by the motion vector of FIG. 6 . The weight is decreased in the area where the velocity is fast (the motion vector is long), and the weight is increased in the area where the velocity is slow (the motion vector is short).
  • the image transform with the spatial filter shall mean that modeling of the transform is performed by convolution of the pixels near the target pixel.
  • a coefficient of the convolution is set at h(l,m).
  • h(l,m) For the sake of convenience, letting ⁇ n ⁇ 1 and m ⁇ n, the transform of the target pixel can be expressed by the following expression (11).
  • h(l,m) itself is referred to as spatial filter or filter coefficient.
  • a property of the transform is determined by the coefficient of h(l,m).
  • the image pickup apparatus such as the digital camera
  • the degradation does not exist in the image forming process
  • only one point has a pixel value except for zero while other pixels except for the one point have the value of zero in the image observed on the image pickup apparatus.
  • the actual image pickup apparatus includes the degradation process, even if the point light source is observed, the image does not become the one point, but the image becomes broadened.
  • the point light source generates the locus according to the camera shake.
  • the spatial filter in which the coefficient is the value proportional to the pixel value of the image observed for the point light source and the summation of the coefficients becomes one, is referred to as Point Spread Function (PSF).
  • PSF Point Spread Function obtained by the motion vector/camera shake function conversion processing unit 22 is used in the embodiment.
  • H* is the general inverse matrix of H
  • H t is the transpose of H
  • ⁇ is a scalar
  • I is a unit matrix having the same size as H t ⁇ H.
  • the image P in which the camera shake is corrected can be obtained from the observed camera shake image P′ by computing the following expression (15) with H*.
  • ⁇ is a parameter for adjusting correction intensity. When ⁇ is small, the correction processing becomes strong. When ⁇ is large, the correction processing becomes weak.
  • P′ H* ⁇ p
  • the size of the image which becomes the original of P is decreased to the relatively small size such as 63 ⁇ 63.
  • P is the matrix of 3969 ⁇ 1
  • H* becomes the matrix of 3969 ⁇ 3969.
  • H* is the matrix which transforms the whole of the image with the blurring into the whole of the corrected image, and a product of each row of H and P corresponds to the computation for performing the correction of each element.
  • the product of the central row of H* and P corresponds to the correction of the original image of the 63 ⁇ 63 pixels with respect to the central pixel.
  • the spatial filter having the size of 63 ⁇ 63 can be formed by generating two-dimensional expression of the central row of H*.
  • the spatial filter formed in the above manner is called general inverse filter (hereinafter referred to as image restoration filter).
  • the spatial filter having the practical size, produced in the above manner, is sequentially applied to each pixel of the whole of the large image, which allows the blurring image to be corrected.
  • the parameter, expressed by ⁇ , for adjusting the restoration intensity also exists in the restoration filter for the blurring image determined by the above procedure.
  • the image restoration processing unit 3 includes filter processing units 31 , 32 , and 33 .
  • the filter processing units 31 and 33 perform the filter processing with a median filter.
  • the filter processing unit 32 performs the filter processing with the image restoration filter obtained by the image restoration filter computing unit 2 .
  • the camera shake image taken by the camera is transmitted to the filter processing unit 31 , and the filter processing is performed with the median filter to reduce noise.
  • the image obtained by the filter processing unit 31 is transmitted to the filter processing unit 32 .
  • the filter processing is performed with the image restoration filter to restore the image having no camera shake from the camera shake image.
  • the image obtained by the filter processing unit 32 is transmitted to the filter processing unit 33 , and the filter processing is performed with the median filter to reduce noise.
  • the ringing reduction processing unit 4 includes an edge intensity computing unit 41 , a weighted average coefficient computing unit 42 , and a weighted average processing unit 43 .
  • the camera shake image taken by the camera is transmitted to the edge intensity computing unit 41 , and edge intensity is computed in each pixel.
  • edge intensity is computed in each pixel. The method of determining the edge intensity will be described.
  • FIG. 8 A 3 ⁇ 3 area centered on a target pixel v 22 is assumed as shown in FIG. 8 .
  • a horizontal edge component dh and a vertical edge component dv are computed for the target pixel v 22 .
  • a Prowitt edge extraction operator shown in FIGS. 9A and 9B is used for the computation of the edge component.
  • FIG. 9A shows a horizontal edge extraction operator
  • FIG. 9B shows a vertical edge extraction operator.
  • the horizontal edge component dh and the vertical edge component dv are determined by the following expressions (16) and (17).
  • dh v 11+ v 12+ v 13 ⁇ v 31 ⁇ v 32 ⁇ v 33
  • dv v 11+ v 21+ v 31 ⁇ v 13 ⁇ v 23 ⁇ v 33
  • edge intensity v_edge of the target pixel v 22 is computed from the horizontal edge component dh and the vertical edge component dv based on the following expression (18).
  • v _edge sqrt( dh ⁇ dh+dv ⁇ dv ) (18)
  • abs(dh)+abs (dv) may be used as the edge intensity v_edge of the target pixel v 22 .
  • a 3 ⁇ 3 noise reduction filter may further be applied to the edge intensity image obtained in the above manner.
  • the edge intensity v_edge of each pixel obtained by the edge intensity computing unit 41 is given to the weighted average coefficient computing unit 42 .
  • th is a threshold for determining whether the edge intensity v_edge is sufficiently strong edge. That is, the edge intensity v_edge and the weighted average coefficient k have a relationship shown in FIG. 10 .
  • the weighted average coefficient computing unit 42 gives the computed weighted average coefficient k of each pixel to the weighted average processing unit 43 .
  • a pixel value of the restoration image obtained by the image restoration processing unit 3 is set at v_restore, and a pixel value of the camera shake image taken by the camera is set at v_shake.
  • the weighted average processing unit 43 performs the weighted average of the pixel value v_restore of the restoration image and the pixel value v_shake of the camera shake image by performing the computation shown by the following expression (20).
  • v k ⁇ v _restore+(1 ⁇ k ) ⁇ v _shake (20)
  • the pixel value v_restore of the restoration image obtained by the image restoration processing unit 3 is directly outputted.
  • the pixel in which the edge intensity v_edge is not more than the threshold th because the ringing of the restoration image is conspicuous as the edge intensity v_edge is decreased, a degree of the restoration image is weakened and a degree of the camera shake image is strengthened.
  • the weighted addition of the restoration image and the camera shake image is performed such that the degree of the restoration image is strengthened in the pixel where the edge intensity v_edge is increased and the degree of the camera shake image is strengthened in the pixel where the edge intensity v_edge is decreased, which reduces the ringing generated on the periphery of the edge portion.
  • the ringing maybe reduced as follows.
  • the image restoration filter (numeral 32 of FIG. 1 ) for the blurring image, there is also a parameter for adjusting the restoration magnitude indicated by ⁇ . Therefore, it is possible that plural kinds of the restoration filters are generated according to the restoration magnitude.
  • the pixel having the large edge intensity v_edge is restored, since the ringing of the corresponding restoration image is inconspicuous, the image is restored with the restoration filter having the high restoration intensity.
  • the pixel having the small edge intensity v_edge is restored, since the ringing of the corresponding restoration image is conspicuous, the image is restored with the restoration filter having the low restoration intensity. Therefore, in the case where the ringing is prevented, it is not necessary to perform the weighted average.

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Cited By (19)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20080085061A1 (en) * 2006-10-03 2008-04-10 Vestel Elektronik Sanayi Ve Ticaret A.S. Method and Apparatus for Adjusting the Contrast of an Input Image
US20080095400A1 (en) * 2004-12-21 2008-04-24 Sony Corporation Image Processing Device, Image Processing Method And Image Processing Program
US20080122953A1 (en) * 2006-07-05 2008-05-29 Konica Minolta Holdings, Inc. Image processing device, image processing method, and image sensing apparatus
US20090021578A1 (en) * 2005-03-22 2009-01-22 Kenji Yamazaki Image Processor and Endoscope Apparatus
US20100189367A1 (en) * 2009-01-27 2010-07-29 Apple Inc. Blurring based content recognizer
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US20120026349A1 (en) * 2010-02-02 2012-02-02 Panasonic Corporation Imaging device and method, and image processing method for imaging device
US20120033096A1 (en) * 2010-08-06 2012-02-09 Honeywell International, Inc. Motion blur modeling for image formation
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US8861852B2 (en) 2011-05-09 2014-10-14 Canon Kabushiki Kaisha Image processing method for image restoration, image processing apparatus and image pickup apparatus
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US20080170124A1 (en) 2007-01-12 2008-07-17 Sanyo Electric Co., Ltd. Apparatus and method for blur detection, and apparatus and method for blur correction
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WO2010131296A1 (ja) * 2009-05-14 2010-11-18 株式会社 東芝 画像処理装置
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JP2012141725A (ja) * 2010-12-28 2012-07-26 Sony Corp 信号処理装置、信号処理方法、およびプログラム
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JP7078895B2 (ja) * 2018-06-11 2022-06-01 オムロン株式会社 制御システム、制御装置、画像処理装置およびプログラム
JP7362284B2 (ja) * 2019-03-29 2023-10-17 キヤノン株式会社 画像処理方法、画像処理装置、プログラム、画像処理システム、および、学習済みモデルの製造方法

Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4220972A (en) * 1979-05-22 1980-09-02 Honeywell Inc. Low contrast object extraction device
US20010008418A1 (en) * 2000-01-13 2001-07-19 Minolta Co., Ltd. Image processing apparatus and method
US20030184663A1 (en) * 2001-03-30 2003-10-02 Yuusuke Nakano Apparatus, method, program and recording medium for image restoration
US6636645B1 (en) * 2000-06-29 2003-10-21 Eastman Kodak Company Image processing method for reducing noise and blocking artifact in a digital image
US20050100241A1 (en) * 2003-11-07 2005-05-12 Hao-Song Kong System and method for reducing ringing artifacts in images
US20050104974A1 (en) * 2002-02-12 2005-05-19 Tatsumi Watanabe Image processing device and image processing method
US7003174B2 (en) * 2001-07-02 2006-02-21 Corel Corporation Removal of block encoding artifacts
US7027661B2 (en) * 2000-11-27 2006-04-11 Sony International (Europe) Gmbh Method of coding artifacts reduction
US7050649B2 (en) * 2001-07-23 2006-05-23 Micron Technology, Inc. Suppression of ringing artifacts during image resizing

Patent Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4220972A (en) * 1979-05-22 1980-09-02 Honeywell Inc. Low contrast object extraction device
US20010008418A1 (en) * 2000-01-13 2001-07-19 Minolta Co., Ltd. Image processing apparatus and method
US6636645B1 (en) * 2000-06-29 2003-10-21 Eastman Kodak Company Image processing method for reducing noise and blocking artifact in a digital image
US7027661B2 (en) * 2000-11-27 2006-04-11 Sony International (Europe) Gmbh Method of coding artifacts reduction
US20030184663A1 (en) * 2001-03-30 2003-10-02 Yuusuke Nakano Apparatus, method, program and recording medium for image restoration
US7003174B2 (en) * 2001-07-02 2006-02-21 Corel Corporation Removal of block encoding artifacts
US7050649B2 (en) * 2001-07-23 2006-05-23 Micron Technology, Inc. Suppression of ringing artifacts during image resizing
US20050104974A1 (en) * 2002-02-12 2005-05-19 Tatsumi Watanabe Image processing device and image processing method
US20050100241A1 (en) * 2003-11-07 2005-05-12 Hao-Song Kong System and method for reducing ringing artifacts in images

Cited By (29)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20080095400A1 (en) * 2004-12-21 2008-04-24 Sony Corporation Image Processing Device, Image Processing Method And Image Processing Program
US7750943B2 (en) * 2004-12-21 2010-07-06 Sony Corporation Image processing device that removes motion blur from an image and method of removing motion blur from an image
US20090021578A1 (en) * 2005-03-22 2009-01-22 Kenji Yamazaki Image Processor and Endoscope Apparatus
US8305427B2 (en) * 2005-03-22 2012-11-06 Olympus Corporation Image processor and endoscope apparatus
US20080122953A1 (en) * 2006-07-05 2008-05-29 Konica Minolta Holdings, Inc. Image processing device, image processing method, and image sensing apparatus
US7898583B2 (en) * 2006-07-05 2011-03-01 Konica Minolta Holdings, Inc. Image processing device, image processing method, and image sensing apparatus
US8131104B2 (en) * 2006-10-03 2012-03-06 Vestel Elektronik Sanayi Ve Ticaret A.S. Method and apparatus for adjusting the contrast of an input image
US20080085061A1 (en) * 2006-10-03 2008-04-10 Vestel Elektronik Sanayi Ve Ticaret A.S. Method and Apparatus for Adjusting the Contrast of an Input Image
US20100189367A1 (en) * 2009-01-27 2010-07-29 Apple Inc. Blurring based content recognizer
US8948513B2 (en) 2009-01-27 2015-02-03 Apple Inc. Blurring based content recognizer
US8929676B2 (en) * 2009-01-27 2015-01-06 Apple Inc. Blurring based content recognizer
US20120026349A1 (en) * 2010-02-02 2012-02-02 Panasonic Corporation Imaging device and method, and image processing method for imaging device
US8553091B2 (en) * 2010-02-02 2013-10-08 Panasonic Corporation Imaging device and method, and image processing method for imaging device
US8803984B2 (en) 2010-02-10 2014-08-12 Dolby International Ab Image processing device and method for producing a restored image using a candidate point spread function
US8520081B2 (en) 2010-02-12 2013-08-27 Panasonic Corporation Imaging device and method, and image processing method for imaging device
US8675079B2 (en) 2010-03-19 2014-03-18 Panasonic Corporation Image capture device, image processing device and image processing program
US20120105658A1 (en) * 2010-04-30 2012-05-03 Panasonic Corporation Imaging device, image processing device, and image processing method
US8553097B2 (en) * 2010-04-30 2013-10-08 Panasonic Corporation Reducing blur based on a kernel estimation of an imaging device
US8600187B2 (en) 2010-08-05 2013-12-03 Panasonic Corporation Image restoration apparatus and image restoration method
US20120033096A1 (en) * 2010-08-06 2012-02-09 Honeywell International, Inc. Motion blur modeling for image formation
US8860824B2 (en) * 2010-08-06 2014-10-14 Honeywell International Inc. Motion blur modeling for image formation
US8905314B2 (en) 2010-09-30 2014-12-09 Apple Inc. Barcode recognition using data-driven classifier
US9396377B2 (en) 2010-09-30 2016-07-19 Apple Inc. Barcode recognition using data-driven classifier
CN102170569A (zh) * 2011-03-24 2011-08-31 深圳市融创天下科技发展有限公司 一种振铃效应去噪方法、装置
US8861852B2 (en) 2011-05-09 2014-10-14 Canon Kabushiki Kaisha Image processing method for image restoration, image processing apparatus and image pickup apparatus
US9826150B2 (en) 2013-10-31 2017-11-21 Fujifilm Corporation Signal processing device, imaging apparatus, parameter generating method, signal processing method, and program
US9892492B2 (en) 2013-10-31 2018-02-13 Fujifilm Corporation Image processing device, imaging apparatus, parameter generating method, image processing method, and non-transitory computer readable recording medium storing a program
CN104796596A (zh) * 2014-01-20 2015-07-22 联想(北京)有限公司 一种信息处理方法及电子设备
US9898807B2 (en) 2014-03-28 2018-02-20 Fujifilm Corporation Image processing device, imaging device, image processing method, and program

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